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Regular Paper | Open Access

Inverse-function-based DC Power Flow Formulation with Loss and Voltage Magnitude Considerations

Dundun Liu1,2 , Mike Zhou3 ( ), Qianhong Wu3, Hongcai Zhang1,2, Yadan Wang1,2
University of Macau Advanced Research Institute in Hengqin, Hengqin 519031, China
State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao 999078, China
State Key Laboratory of Power Grid Safety and Energy Conservation, China Electric Power Research Institute, Beijing 100192, China
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Abstract

This paper introduces a novel DC power flow model that incorporates network loss and voltage magnitudes, aiming to achieve comparable calculation results to AC power flow while mitigating convergence issues. The proposed model addresses network loss by formulating a fixed-point equation for voltage angles, effectively accounting for the impact of network losses through the introduction of a virtual load. Furthermore, an inverse function of the complex power equation is derived to calculate voltage magnitudes, with the voltage angles serving as input. To evaluate the performance of the proposed model, extensive case studies are conducted on various test systems, ranging from small-scale to large-scale networks, including a real-world power grid. The results demonstrate the high accuracy of the proposed DC power model regarding voltage angles and magnitudes, active and reactive power. Importantly, the model showcases its ability to overcome power flow convergence issues, providing reliable and precise solutions in situations where the traditional Newton-Raphson algorithm fails to converge. The findings of this research offer a promising alternative for power flow calculations and adjustments in real-world applications.

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CSEE Journal of Power and Energy Systems
Pages 1887-1895

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Cite this article:
Liu D, Zhou M, Wu Q, et al. Inverse-function-based DC Power Flow Formulation with Loss and Voltage Magnitude Considerations. CSEE Journal of Power and Energy Systems, 2026, 12(4): 1887-1895. https://doi.org/10.17775/CSEEJPES.2023.02500

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Received: 30 March 2023
Revised: 22 June 2023
Accepted: 09 August 2023
Published: 14 February 2024
© 2023 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).